R Notebook

Within-host diversity and minor variant analyses in samples from Houston Methodist Hospital, 2021

Sample characteristics and inclusion criteria

##### Load libraries and define recurring functions / variables
source("./scripts/startup.R")

#all mcov samples we have ever received - reformat sample names to be consistent
mcov_samples_all <- fread("full_lineage_report_20220507.tsv", data.table=F) %>% 
  mutate(MCoVNumber=mcov_reformat(taxon)) %>% 
  filter(startsWith(MCoVNumber, "MCoV")) %>% 
  filter(MCoVNumber!="MCoV30904") #remove one sample with inconsistently formatted duplicates

#run and date info; keep only earliest sample from the same patient
mcov_info <- fread("sample_date_and_run.csv", data.table=F) %>% 
  mutate(COLLECTION_DT=as.Date(COLLECTION_DT, "%m/%d/%y"), 
         MCoVNumber=str_remove(mcov_id, "-")) %>% 
  arrange(COLLECTION_DT) %>% filter(!duplicated(PatientID))

#samples we received that we're interested in (December 2020-November 2021)
mcov_samples_1 <- mcov_samples_all %>% filter(!taxon %in% dup65.66) %>% 
  filter(MCoVNumber %in% mcov_info$MCoVNumber) %>% left_join(mcov_info)

Joining, by = "MCoVNumber"

#all samples sequenced in each run (including those from outside of this study period) -- sometimes relevant for run QC purposes
runs_all_samples <- fread("run_samples.csv", data.table = F) %>% 
  mutate(MCoVNumber=str_remove(`Sample ID`,"-"), run=Run) %>% 
  select(run, MCoVNumber)

#summary stats on coverage of each sample; drop the run 65 duplicates and join coverage info to main dataset
d1 = fread('coverage_levels_20220507.csv', data.table = F) %>% 
  filter(duplicated(samplename)) %>% pull(samplename)
d2 = fread('coverage_levels_20220507.csv', data.table = F) %>% 
  filter(samplename %in% d1) %>% filter(!duplicated(samplename)) %>% 
  pull(filename)
coverage_levels <- fread('coverage_levels_20220507.csv', data.table = F) %>% 
  filter(!filename %in% d2) %>% filter(samplename!="MCoV30904") %>% select(-1)

# join dataframes
mcov_samples<-mcov_samples_1 %>% 
  select(MCoVNumber, lineage, scorpio_call, qc_status, 
         COLLECTION_DT, INSTRUMENT, INSTRUMENT_RESULT, run=run_group) %>% 
  left_join(coverage_levels, by=c("MCoVNumber"="samplename"))

mcov_samples_with_ct<-mcov_samples %>% filter(INSTRUMENT_RESULT<50) %>% 
  mutate(CT=INSTRUMENT_RESULT)
#what's the relationship between CT value and read coverage?
coverage_all<-mcov_samples_with_ct %>% ggplot(aes(x=CT, y=median_coverage)) + 
  geom_point(alpha=0.07) + theme_bw() + xlab("Ct value") + ylab("Sample median depth")
#how does coverage in high-CT samples compare with the rest of them?
coverage_ct_cat<-mcov_samples_with_ct %>% mutate(sample_ct=if_else(CT>=40, "CT>=40", "CT<40")) %>% 
  ggplot(aes(x=sample_ct, y=median_coverage)) + 
  geom_point(alpha=0.2, position=position_jitter(width=0.25)) + 
  geom_boxplot(color="red", alpha=0) + theme_bw() + 
  xlab("Ct value") + ylab("Sample median depth")
#see that CT>=40 samples tend to have extremely low coverage, but some outliers
#SUPP FIG 12
#coverage_pre_filtering<-plot_grid(coverage_all, coverage_ct_cat)
#coverage_pre_filtering

Run-level QC

#are there any runs where high-CT samples have unusually high coverage? treat CT>40 samples as negative controls and eliminate runs where their coverage is not different from those of the rest of the samples
test_group<-mcov_samples_with_ct %>% mutate(is_neg=if_else(CT>=40,1,0)) %>% 
  group_by(run) %>% mutate(n_negs=sum(is_neg)) %>% filter(n_negs>=3) %>% 
  ungroup() %>% mutate(sampletype=if_else(is_neg==1, "negctrl","sample"))
runs_to_drop1 = test_group %>% group_by(run) %>% 
  summarise(t_test_p=t.test(fraction_1000x_coverage~sampletype)$p.value) %>% 
  arrange(desc(t_test_p)) %>% filter(t_test_p>0.01) %>% pull(run)
runs_to_drop2 = test_group %>% group_by(run) %>% 
  summarise(t_test_p=t.test(median_coverage~sampletype)$p.value) %>% 
  arrange(desc(t_test_p)) %>% filter(t_test_p>0.01) %>% pull(run)

############ 
runs_to_drop<-union(runs_to_drop1, runs_to_drop2)

mcov_samples_with_ct %>% pull(run) %>% unique()

 [1] "Run_11" "Run_12" "Run_15" "Run_13" "Run_16" "Run_18" "Run_19" "Run_20" "Run_21" "Run_22" "Run_23" "Run_24" "Run_25" "Run_26" "Run_27"
[16] "Run_28" "Run_29" "Run_30" "Run_31" "Run_35" "Run_32" "Run_33" "Run_34" "Run_37" "Run_36" "Run_39" "Run_40" "Run_41" "Run_43" "Run_44"
[31] "Run_46" "Run_48" "Run_49" "Run_51" "Run_52" "Run_53" "Run_57" "Run_58" "Run_59" "Run_61" "Run_62" "Run_63" "Run_64" "Run_65" "Run_66"
[46] "Run_68" "Run_69" "Run_70" "Run_71" "Run_72" "Run_73" "Run_74" "Run_75" "Run_76" "Run_77" "Run_78" "Run_79" "Run_80" "Run_81" "Run_82"
[61] "Run_83" "Run_85" "Run_86" "Run_87" "Run_88" "Run_89" "Run_90" "Run_14" "Run_17"

test_group %>% group_by(run, sampletype) %>% summarize(counts = n()) %>% 
  filter(run %in% runs_to_drop) %>% nrow()

[1] 44

mcov_samples_with_ct %>% pull(run) %>% unique()

 [1] "Run_11" "Run_12" "Run_15" "Run_13" "Run_16" "Run_18" "Run_19" "Run_20" "Run_21" "Run_22" "Run_23" "Run_24" "Run_25" "Run_26" "Run_27"
[16] "Run_28" "Run_29" "Run_30" "Run_31" "Run_35" "Run_32" "Run_33" "Run_34" "Run_37" "Run_36" "Run_39" "Run_40" "Run_41" "Run_43" "Run_44"
[31] "Run_46" "Run_48" "Run_49" "Run_51" "Run_52" "Run_53" "Run_57" "Run_58" "Run_59" "Run_61" "Run_62" "Run_63" "Run_64" "Run_65" "Run_66"
[46] "Run_68" "Run_69" "Run_70" "Run_71" "Run_72" "Run_73" "Run_74" "Run_75" "Run_76" "Run_77" "Run_78" "Run_79" "Run_80" "Run_81" "Run_82"
[61] "Run_83" "Run_85" "Run_86" "Run_87" "Run_88" "Run_89" "Run_90" "Run_14" "Run_17"

test_group %>% group_by(run, sampletype) %>% summarize(counts = n()) %>% 
  filter(sampletype=="negctrl") %>% arrange(counts) %>% mutate(dropped = run %in% runs_to_drop) %>%
  ggplot(aes(dropped,counts, label=run)) + geom_boxplot() + geom_point() + geom_text_repel() + theme_pubr()

runs_kept = mcov_samples_with_ct$run[!mcov_samples_with_ct$run %in% runs_to_drop] %>% unique()
# for (i in runs_kept) {
#   f = mcov_samples_with_ct %>% filter(run == i) %>% ggplot(aes(INSTRUMENT_RESULT, fraction_1000x_coverage)) + geom_point() + ggtitle(i) + theme(plot.title = element_text(size = 40, face = "bold"))
#   print(f)
# }

f1 = mcov_samples_with_ct %>% ggplot(aes(INSTRUMENT_RESULT, fraction_1000x_coverage)) + 
  geom_point(shape=".", alpha = 0.5) + theme_pubr() + 
  theme(plot.title = element_text(size = 40, face = "bold")) + 
  geom_vline(xintercept=40, color = "red")
f1 = ggMarginal(f1 + rremove("xlab"))

f2 = mcov_samples_with_ct %>% ggplot(aes(INSTRUMENT_RESULT, log10(median_coverage))) + 
  geom_point(shape=".", alpha = 0.5) + theme_pubr() + 
  theme(plot.title = element_text(size = 40, face = "bold")) + 
  geom_vline(xintercept=40, color = "red")
f2 = ggMarginal(f2)

ggarrange(f1, f2, ncol = 1, nrow = 2, align = "v")

# tmp = data.table(mcov_samples_with_ct %>% filter(CT>35))[ , invisible(cor.test(fraction_1000x_coverage, -CT, method="spearman")[-2]), by=run] %>% select(run, p.value, estimate) %>%
#   mutate; tmp
# tmp = tmp %>% mutate(abnormal = run %in%   
#        c("Run_20","Run_89","Run_21","Run_58","Run_76","Run_71","Run_75",
#        "Run_86","Run_74","Run_70","Run_78","Run_62","Run_13","Run_85",
#        "Run_65","Run_34","Run_87","Run_11","Run_83","Run_16","Run_77",
#        "Run_81"))
# ggplot(tmp, aes(estimate, -log10(p.value), color = abnormal, 
#                 label = gsub("Run_", "", run))) + 
#          geom_point() + geom_text_repel()+ theme_pubr()
# 
# tmp %>% arrange(-p.value)
# # runs 2

### plot boxplot of the runs
stmp = mcov_samples_with_ct %>% mutate(abnormal = run %in%   
    runs_to_drop) %>% mutate(run = gsub("Run_", "", run)) %>% 
    mutate(is_neg=if_else(CT>=40,1,0))

f3 = ggplot(stmp, aes(run, fraction_1000x_coverage, color = abnormal, fill = abnormal)) + 
  geom_boxplot() + 
  geom_point(data = stmp %>% filter(is_neg == T), 
             aes(run, fraction_1000x_coverage), color = "red", alpha = .9, 
             shape = 1) + 
  scale_fill_grey(start = 1, end = 0.8) + 
  scale_color_grey(start=0, end=0.6) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1), 
        text = element_text(size=8)) +
  labs( x = NULL)
  
f4 = ggplot(stmp, aes(run, log10(median_coverage), 
                      color = abnormal, fill = abnormal)) + 
  geom_boxplot() + 
  geom_point(data = stmp %>% filter(is_neg == T), 
             aes(run, log10(median_coverage)), color = "red", alpha = .9, 
             shape = 1) + 
  scale_fill_grey(start = 1, end = 0.8) + 
  scale_color_grey(start=0, end=0.6) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1), 
        text = element_text(size=8))
  
ggarrange(f3, f4, ncol = 1, nrow = 2, align = "v", common.legend = T)

Sample-level QC

#nextclade QC
nc<-fread("houston_nextclade.tsv", sep='\t', data.table = F) %>% 
  mutate(MCoVNumber=regmatches(seqName, regexpr("[M,R,S,O]CoV.[0-9]+", seqName)) %>% 
           str_remove("-") %>% str_remove("_")) %>% filter(!duplicated(MCoVNumber))
nextclade_bad_samples<-nc %>% filter(qc.overallStatus %in% c("bad")) %>% pull(MCoVNumber)
#drop bad runs and samples that don't pass pangolin QC or nextclade QC
mcov_samples_filtered<-mcov_samples %>% filter(!run %in% runs_to_drop) %>% 
  filter(qc_status=="pass") %>% filter(!MCoVNumber %in% nextclade_bad_samples) %>% 
  filter(scorpio_call!="Omicron (BA.1-like)") %>% 
 ##### #main coverage criterion for fair comparisons: X depth over Y percent of the genome
  filter(fraction_100x_coverage>=0.98) %>% droplevels()

#what's the distribution of CT values?
ct_distribution_after_qc<-mcov_samples_filtered %>% filter(INSTRUMENT_RESULT<50) %>% 
  ggplot(aes(x=INSTRUMENT_RESULT)) + geom_histogram(binwidth=1) + theme_bw() + 
  xlab("Ct value") + labs(caption="After exclusion criteria")

nrow(mcov_samples_filtered %>% filter(INSTRUMENT_RESULT < 26))

[1] 6140

ggsave("ggsave/plot1_ct_before_after.pdf", plot1_ct_before_after, height = 3, width = 4)

Error in plot_theme(plot) : object 'plot1_ct_before_after' not found

Preliminary minor variant distributions

#nucleotide positions of all primers used in dataset; will exclude 
primers = fread("nCoV-2019.artic_v3.primer.txt", sep="\t", header=FALSE, data.table=F) %>% 
  select(start = V2, end = V3)
primer_positions_v3<-as.numeric()
for (i in 1:nrow(primers)){
  primer_positions_v3<-c(primer_positions_v3, primers[i,]$start:primers[i,]$end)
}

primers = fread("nCoV-2019.artic_v4.primer.bed", sep="\t", 
                header=FALSE, data.table = F) %>% select(start = V2, end = V3)
primer_positions_v4<-as.numeric()
for (i in 1:nrow(primers)){
  primer_positions_v4<-c(primer_positions_v4, primers[i,]$start:primers[i,]$end)
}

primers = fread("V4.1.bed", sep="\t", header=FALSE, data.table=F) %>% 
  select(start = V2, end = V3)
primer_positions_v4.1 <- as.numeric()
for (i in 1:nrow(primers)){
  primer_positions_v4.1 <- c(primer_positions_v4.1, primers[i,]$start:primers[i,]$end)
}

primer_positions_all <- c(primer_positions_v3, primer_positions_v4, primer_positions_v4.1) %>% unique()

#reference genome with nucleotide positions of genes
genes <- fread("ntpos_gene_update.csv", data.table = F)
gene_names <- genes %>% pull(gene_id) %>% unique()
genes$gene_id <- factor(genes$gene_id, levels = gene_names)

### Update this if you change sample inclusion criteria
#minor_variant_sites_allLevels <- fread("minor_sites_100x_all_20220507.csv", data.table=F) %>% mutate(MCoVNumber=regmatches(name, #regexpr("[M,R,S,O]CoV.[0-9]+", name)) %>% str_remove("-") %>% str_remove("_")) %>% filter(MCoVNumber %in% #mcov_samples_filtered$MCoVNumber)
#write.csv(minor_variant_sites_allLevels, "minor_sites_workingsamples_100-98minimum.csv", row.names=FALSE)

### Update this if change the thresholds for counting minor variants
#file was already filtered to sites with minimum 100 reads depth and A,C,T,G minor variant present and binomial significance check passed. Further filtering:
#depth_at_site<-100
#minor_frequency<-0.01
#total_minor_reads<-50
#
#minor_variant_sites_threshold_applied<-fread("minor_sites_workingsamples_100-98minimum.csv", data.table = F) %>%
#  filter(totalcount>=depth_at_site) %>%
#  filter(!ntpos %in% 1:265) %>% filter(!ntpos>29674) %>% #don't include 5' and 3' UTR
#  filter(!ntpos %in% primer_positions_all) %>% #don't include primer binding sites
#  filter(major %in% c("A","C","T","G")) %>% #don't want minor variants at consensus deletion sites
#  filter(minorfreq>=minor_frequency) #%>%
#  #filter(minorfreq*totalcount>=total_minor_reads) 

#write.csv(minor_variant_sites_threshold_applied, 'minor_variants_filtered_100x0.01_50.csv')

#load file that was generated/saved above
minor_variant_sites_threshold <- fread('minor_variants_filtered_100x0.01_50.csv', data.table=F) 
minor_variant_sites_threshold %>% pull(MCoVNumber) %>% unique %>% length

[1] 14536

Overall minor variant richness

n_var <- minor_variant_sites_threshold %>% group_by(MCoVNumber) %>% tally() %>% arrange(desc(n)) #this tally doesn't include any samples with 0 variants, so need to join to original list
samples_n_var <- mcov_samples_filtered %>% left_join(n_var) %>% arrange(COLLECTION_DT) %>% mutate(n_var=tidyr::replace_na(n,0)) 

Joining, by = "MCoVNumber"

#what's the distribution of minor variant richness?
### 
((
  n_var_select <- samples_n_var %>% ggplot(aes(x=n_var)) + 
  geom_histogram(binwidth=5) + theme_pubr() + 
  xlab("No. minor variants in sample") + ylab("No. samples") + 
  scale_y_continuous(trans='log1p', breaks=c(1, 10, 100, 1000, 5000))
))

ggsave("ggsave/plot1_target.pdf", plot = plot1_target, height = 4, width = 4)

Error in plot_theme(plot) : object 'plot1_target' not found

# test_ct = patient_data %>% filter(INSTRUMENT_RESULT<40) %>% mutate(vaccine = if_else(Vaccine_Status=="No vaccine", 0, 1)) %>% ggplot(aes(x=INSTRUMENT_RESULT, y=n_var, color = as.factor(vaccine))) + geom_point(shape = 1, alpha = 1/8) + geom_density_2d(alpha = 1/2) + scale_y_continuous(trans = "log2", breaks = c(0,1,2,4,8,16,32,64, 128, 256))+
#   scale_x_continuous(breaks = seq(0,40,by = 5)) +
#   theme_pubr() + xlab("Ct value") + ylab("No. minor variants") + geom_smooth(method=lm)
# ggMarginal(test_ct, groupColour = TRUE, groupFill = TRUE, type = "violin", draw_quantiles = c(.5))

# ggdraw(n_var_select + theme_half_open(12)) +
#    draw_plot(n_var_ct_zoom, x=0.3, y=0.3, width=0.7, height=0.7) +
#    draw_plot_label(
#      c("a", "b"),
#      c(0, 0.3),
#      c(1, 1),
#      size = 12
#    )

samples_n_var %>% pull(n_var) %>% summary()

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    3.00    6.00   18.94   16.00  452.00 

Reproducibility of minor variants

all_replicates_table_filt <- fread("replicated_samples.csv", data.table=F) %>% filter(MCoVNumber %in% samples_n_var$MCoVNumber)

|--------------------------------------------------|
|==================================================|

minors_table<-all_replicates_table_filt %>% 
  filter(major.original %in% c("A","C","T","G")) %>% 
  filter(minor.original %in% c("A","C","T","G")) %>%
  filter(totalcount.original>=100 & minorfreq.original>=0.01 & totalcount.original*minorfreq.original>=50 & 
           tolower(binocheck.original)!="false") %>% filter((!ntpos %in% primer_positions_all)) %>% 
  filter(!ntpos %in% 1:265) %>% filter(!ntpos>29674)
#was the same minor variant found in the second replicate? if not, set minor frequency to 0 in second rep
minors_table<-minors_table %>% 
  mutate(minorfreq.reseq=if_else(minor.original==minor.reseq, minorfreq.reseq, 0)) %>% 
  mutate(detected_minor_in_repl=if_else(minor.original==minor.reseq,"yes","no"))

#how well are minor variants recovered in samples with different Ct values?
minors_table <- minors_table %>% left_join(select(mcov_samples, MCoVNumber, CT=INSTRUMENT_RESULT)) %>% 
  mutate(sampleCT_bin = case_when(CT<26 ~ "below 26",
                                CT>=26&CT<=35 ~"CT 26-35",
                                CT>35&CT<50 ~"greater than 35",
                                CT>100~"unknown", #aptima instrument uses RLU not CT
                                is.na(CT) ~ "unknown")) 

Joining, by = "MCoVNumber"

((
  rep_ct<-minors_table %>% 
  ggplot(aes(x=minorfreq.original, y=minorfreq.reseq, color=detected_minor_in_repl)) + 
  scale_color_manual(values=c("red","black")) + geom_point(alpha=0.5) + 
  facet_wrap(~sampleCT_bin) + theme_bw() + labs(x="MAF in replicate 1", y="MAF in replicate 2") + 
  theme(legend.position="bottom")
))

#how well are minor variants recovered in samples with different median coverage?
rep_depth <- minors_table %>% left_join(coverage_levels, by=c("MCoVNumber"="samplename")) %>% 
  mutate(coverage_bin=cut(median_coverage, 4)) %>% 
  ggplot(aes(x=minorfreq.original, y=minorfreq.reseq, color=detected_minor_in_repl)) + 
  scale_color_manual(values=c("red","black")) + geom_point(alpha=0.5) + 
  facet_wrap(~coverage_bin) + theme_bw() + 
  labs(x="MAF in replicate 1", y="MAF in replicate 2") + 
  theme(legend.position="none", axis.text.x = element_text(color=c(1,0,1,0))) + 
  labs(caption="Reproducibility in samples with different median depths")

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

#in the range of Ct values/coverage observed here, reproducibility seems more associated with Ct than with coverage 

#what's the distribution of depth/MAF in reproducible vs. non-reproducible minor variants?
rep_depth_freq <- minors_table %>% ggplot(aes(x=totalcount.original, y=minorfreq.original)) + 
  geom_point(alpha=0.5) + facet_grid(detected_minor_in_repl~.) + theme_bw() + 
  theme(axis.text.x = element_text(color=c(1,0,1,0))) + xlab("Seq depth in rep 1") + 
  ylab("MAF in rep 1") + labs(caption="Reproducibility at sites with different depth and MAF")

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

#depth and frequency of minor variants is also not very different between reproducible and non-reproducible variants

rep_mutations <- minors_table %>% filter(detected_minor_in_repl=="yes") 
nonrep_mutations <- minors_table %>% filter(detected_minor_in_repl=="no") 

a <- table(rep_mutations$major.original, rep_mutations$minor.original) %>% 
  data.frame() %>% ggplot(aes(x=Var1, y=Var2, fill=Freq)) + 
  geom_tile(colour = "black") + # grid colour
  scale_fill_gradient(low = "white",
                      high = "steelblue") +
  theme_minimal() + labs(fill = "Number",
       x = "Consensus allele",
       y = "Minor allele", caption="Reproducible minor variants")

b <- table(nonrep_mutations$major.original, nonrep_mutations$minor.original) %>% 
  data.frame() %>% ggplot(aes(x=Var1, y=Var2, fill=Freq)) + 
  geom_tile(colour = "black") + 
  scale_fill_gradient(low = "white",
                      high = "steelblue") +
  theme_minimal() + labs(fill = "Number",
       x = "Consensus allele",
       y = "Minor allele", caption="Non-reproducible minor variants")

rep_nucleotides <- cowplot::plot_grid(a, b, ncol=2)

#SUPP FIG 8
rep_nucleotides

rep_indiv_samples <- minors_table %>% ggplot(aes(x=minorfreq.original, 
                                               y=minorfreq.reseq, 
                                               color=detected_minor_in_repl)) + 
  geom_point(alpha=0.5) + scale_color_manual(values=c("red","black")) + 
  facet_wrap(CT~MCoVNumber) + theme_bw() + xlab("MAF in replicate 1") + 
  ylab("MAF in replicate 2") + 
  theme(legend.position="none", axis.text.x = element_text(color=c(1,0,1,0)))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

#SUPP FIG 1
plot_grid(
  plot_grid(rep_ct, 
            plot_grid(rep_depth, rep_depth_freq, ncol=2, rel_widths=c(1.2,1), 
                      labels=c("b","c")), nrow=2, labels=c("a",NA)), 
  rep_indiv_samples, labels=c(NA, "d"), rel_widths=c(1.1,1))

Warning: Removed 1 rows containing missing values (`geom_text()`).

Will limit analyses to samples with CT<26, where we are more confident in reproducibility of minor variant

samples_n_var %>% filter(INSTRUMENT_RESULT<26 & n_var < 30) %>% pull(n_var) %>% quantile(c(0.5,0.7))

50% 70% 
  5   7 

samples_to_analyze <- samples_n_var %>% filter(INSTRUMENT_RESULT<26) %>% pull(MCoVNumber)
lineages_figure <- mcov_samples_filtered %>% 
  filter(MCoVNumber %in% samples_to_analyze) %>% 
  mutate(variant=case_when(startsWith(scorpio_call,"Delta") ~ "Delta",
        startsWith(scorpio_call,"Alpha") ~ "Alpha",
        !(startsWith(scorpio_call,"Delta")|
            startsWith(scorpio_call,"Alpha")) ~ "other lineages")) %>%
  mutate(variant=if_else(lineage == "B.1.2","B.1.2",variant)) %>%
  ggplot(aes(x=COLLECTION_DT)) + geom_bar(aes(fill=variant)) + theme_bw() + 
  scale_fill_manual(values=c("#00A08A", "#F2AD00", "#F98400", "#5BBCD6")) + 
  xlab("Collection date") + ylab("No. samples") + 
  ggtitle(paste0('Final sample set, CT<26, n =',length(samples_to_analyze))) + 
  scale_x_date(minor_breaks="1 month")

#SUPP FIG 2
lineages_figure

samples_n_var %>% filter(INSTRUMENT_RESULT<26) %>% pull(n_var) %>% summary()

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    2.00    5.00   10.79   10.00  379.00 

#plot_grid(coverage_pre_filtering, ct_inclusion, ncol=2, labels=c("a","b"))

minor_sites_lowct<-minor_variant_sites_threshold %>% left_join(mcov_samples_filtered) %>% 
  filter(INSTRUMENT_RESULT<26) 

Joining, by = "MCoVNumber"

What kinds of run-specific effects do we see even after filtering for high-quality samples?

((n_var_by_run <- samples_n_var %>% filter(INSTRUMENT_RESULT < 26 & n_var < 30) %>% 
    ggplot(aes(x=run, y=n_var)) + geom_boxplot() + theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + xlab("Run") + ylab("No. minor variants")))

#are run effects related to sequencing depth?
n_var_by_coverage<-samples_n_var %>% filter(INSTRUMENT_RESULT<26) %>% ggplot(aes(x=median_coverage, y=n_var)) + geom_point(alpha=0.5) + geom_smooth() + theme_bw() + xlab("Sample median coverage") + ylab("No. minor variants in sample")
#not on the individual sample level

#is average minor variant richness in a run related to average depth of coverage in the run?
n_var_depth_averages<-samples_n_var %>% filter(INSTRUMENT_RESULT<26) %>% group_by(run) %>% summarise(median_n_var=median(n_var), median_sample_coverage=median(median_coverage), median_ct=median(INSTRUMENT_RESULT)) %>% ggplot(aes(x=median_sample_coverage, y=median_n_var)) + geom_point() + theme_bw() + xlab("Run-level median of median coverage") + ylab("Median no. minor variants of samples in run") + geom_smooth(method=lm) 

samples_n_var %>% filter(INSTRUMENT_RESULT<26) %>% filter(run=="Run_90") %>% ggplot(aes(x=median_coverage, y=n_var)) + geom_point() + ggtitle("Run 90 - very high average coverage") + theme_bw() + geom_hline(yintercept=10, linetype="dotted")

samples_n_var %>% filter(INSTRUMENT_RESULT<26) %>% filter(run=="Run_29") %>% ggplot(aes(x=median_coverage, y=n_var)) + geom_point() + ggtitle("Run 29 - lower average coverage") + theme_bw() + geom_hline(yintercept=10, linetype="dotted")

#are the run-specific differences in coverage stronger in samples that were included or that weren't included?
mcov_samples %>% mutate(hq_sample=if_else(MCoVNumber %in% minor_sites_lowct$MCoVNumber, "hq","excluded")) %>%  ggplot(aes(x=run, y=median_coverage)) + geom_boxplot() +theme_bw() + theme(axis.text.x=element_text(angle=90)) + facet_grid(hq_sample~.) 

#SUPP FIG 3
plot_grid(n_var_by_run, plot_grid(n_var_by_coverage, n_var_depth_averages, nrow=2), ncol=2, rel_widths = c(2,1), labels=c("a","b"))

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'`geom_smooth()` using formula = 'y ~ x'